In an effort to maximize profit and profit potential, farmers and farm managers use a variety of methods in deciding what crops to grow for a given amount of acreage. Some farmers have more options at their disposal, and hence more complex decisions must be made. Accordingly, some farmers' methods are more structured and rigorous than others. Additionally biological and climatological factors constrain farmers' crop selection options. Moreover, economic factors, such as, for example, market prices, participation in government farm subsidy programs or conditions for credit, often influence or dictate crop selection and acreage allocation (i.e. the amount of land devoted to a specific crop). For purposes of the present invention, acreage allocation refers to the amount of land devoted to a specific crop. Finally, subjective elements, such as, for example, tolerance for risk, willingness to experiment, use of technology, knowledge and experience affect crop selection decisions.
Crop selection decisions are normally comprised of three primary elements—objectives, information and constraints. Farm planning decisions are governed by trade-offs between multiple objectives such as, for example, profit maximization (i.e., a collection of decisions and activities that result in the highest returns on assets), risk minimization (i.e., a collection of decisions and activities that result in the least risk given present uncertainties and potential outcomes), desire for independence and inter-generational stewardship (i.e., a collection of decisions and activities which result in the greatest environmental enhancement and the least ecological damage from agricultural activities). The relativistic level of importance of each of these objectives varies for each farmer. Information and advice on crops and markets are also available from extension agents, agricultural lenders, commodity groups, friends, neighbors, private information services and consultants. Finally, the allocation of crops to acreage may be constrained by such factors as, for example, feasible crop types, rotation patterns, resource availability, economic and market conditions and an individual's tolerance of market and natural risk.
With regard to constraints, farmers are limited to specific crop alternatives by such factors as, for example, soil characteristics and climate that is primarily dictated by geography. This translates into an agronomic (i.e., biological) viability and a regional comparative advantage. For purposes of the present invention, viable crops are crops that, based on the given constraints, can be grown on a given farm with at least a minimum economic return. The farmer may further be limited by availability and access to various resources such as, for example, capital, land, water, labor, machinery, etc.
To select which crops to plant, as well as the most optimum amount of the crop, the farmer estimates production costs and projects crop market prices and yield to calculate an expected rate of return for a given crop. The farmer assesses the variance of projected prices and yields. Contracting for a crop can alleviate market uncertainty; however, crop yield uncertainty is a function of uncontrollable factors inherent in agriculture, such as, for example, weather and blight.
Finally, the farmer must consider risks. Risk management strategies include, for example, crop rotation, crop diversification, forward contracting (i.e., the practice of selling a crop prior to harvest and/or prior to planting; such a practice reduces market risk by establishing a fixed price) and financial instruments such as, for example, futures options (i.e., contracts giving one party the right to buy/sell a commodity at a particular price during a specific time frame; options are used to hedge risk by balancing an investment position). There are other dimensions of risk including, for example, willingness to implement unproven practices, early adoption of new technologies, new crop varieties and new marketing methods.
Farmers who qualify for participation in government farm programs (i.e., by growing crops covered under such programs and complying with program restrictions) often maximize their return on investment and minimize their risk by maximizing the revenue available under these programs. Should these programs be reduced or eliminated, many farmers' decision making processes will probably change due to a changed risk profile, as the farmer will most likely assume more market and production risk.
Farmers developing cropping strategies, in addition to consideration of the above factors, must understand controllable factors such as, for example, crop mixes and rotations, input quantities (chemical and water applications) and management practices and field operations (e.g., tillage, plant spacing and harvesting). Farmers developing cropping strategies must also consider uncontrollable factors such as, for example, weather and markets. Assessment of controllable and uncontrollable factors translates into additional constraints and objectives. That must be considered by farmers developing cropping strategies.
Production and market information are evaluated in the context of the farmer's objectives to frame crop selection decisions. The crucial decisions primarily consist of how many acres of each crop to plant in the context of the stated objectives of profit maximization, risk minimization and stewardship. These decisions can become quite complex, depending on such factors as, for example, the number of crops under consideration, the length of the growing season, rotation patterns, available resources, variability of price and yield, etc. Careful planning and decision-making are critical to profitable farming. The planning phase of the annual cropping cycle is the point at which the farmer has the most leverage to influence profit potential.
There are few computer-based tools to help farmers during these critical planning and decision making periods. Although inexpensive and powerful personal computers are readily available to farmers and farm managers, decision support software for crop selections has not been developed. While a number of firms market agricultural-related personal computer software, such products primarily perform record-keeping and accounting functions. Optimization algorithms are not utilized near their potential for decision analysis for farm planning and crop selections.
Mathematical modeling software (i.e., the process of constructing and solving algebraic equations to gain insight into an issue and the potential outcomes of proposed actions), and algorithms such as linear programming (i.e., a method for representing a problem as a system of interdependent linear equations), integer programming (a type of linear programming where solution variables must be whole numbers), mixed integer programming (a mathematical representation where some solution variables are integers and some are not); and dynamic programming, exist and are used in other industries. These modeling tools are used in some areas of agriculture for functions such as livestock feed mixing (see Markley, U.S. Pat. No. 3,626,377), but for the most part these modeling tools have not found widespread use in the field of production agriculture.
Purdue University and other land-grant universities' extension programs have in the past attempted to introduce area farms to linear programming models. In the U.S. Department of Agriculture's 1989 Yearbook of Agriculture (pp. 147), Howard Doster, Extension Economist at Purdue discusses the application of linear programming models to farm management decisions such as machinery allocation. However, such models were run on a large mainframe computer rather than a personal computer. Several land-grant universities have developed software for farm planning based upon crop budgeting rather than optimization techniques such as mathematical programming. Some of these programs help farmers maximize revenue by structuring their crop selection decisions based on revenue enhancing opportunities provided by Government farm programs. However, the utility of much of this software is often geographically limited. The large number of variables inherent in agricultural enterprises, and the regional variability, imposes limitations upon the utility of existing software outside of the region.
Further, none of the existing programs utilize data from third party industry professionals or sources, such as input supply retailers, manufacturers of seed and crop protection products, crop consultants, crop insurance agents, agricultural lenders, marketing advisors, agricultural certified public accountants and agricultural equipment dealers. The information and data provided by these sources are necessary to obtain the optimal farm management plan for a particular farmer.
Therefore, a need exists for a system that overcomes the above-stated disadvantages.